| dc.description.abstract |
Recent years have seen a widespread growth of research in the Internet of Things (IoT). While mobility networks such as the Intelligent Transportation Systems (ITS) are being increasingly studied for their application in smart cities, there are numerous cyber threats that may disrupt the security and safety of the users of such networks. This study proposes an intelligent, statistical Intrusion Detection System (IDS) called Multi-branch Reconstruction Error (MbRE) for the long term security of ITS against known and unknown threats. The proposed IDS learns only from normal behavior, detects deviation of vehicular from it, and classifies it into eight generalized buckets based on the aspects of the data found to be malicious, i.e. frequency, identity and motion (speed and position). The results obtained show the success of the proposed IDS in detecting different threats with recall and accuracy scores between 97.5% to 100% without the need to train on them. |
en_US |